Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Developing personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …

Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an
important role in treating multiple complex diseases. Yet, our ability to identify and validate …

Motivation The use of drug combinations, termed polypharmacy, is common to treat patients
with complex diseases or co-existing conditions. However, a major consequence of …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

Recent advancements in experimental high-throughput technologies have expanded the
availability and quantity of molecular data in biology. Given the importance of interactions in …